基于时间序列与GWO-ELM模型的滑坡位移预测
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摘要
针对三峡库区的阶跃型滑坡位移特征,以白水河滑坡为例,提出一种基于时间序列和灰狼优化的极限学习机(GWO-ELM)位移预测模型。首先,根据滑坡的内在演化规律和外部影响因素,建立滑坡位移的时间序列模型,将监测位移分解为趋势性位移和周期性位移,并运用稳健加权最小二乘法的三次多项式对趋势性位移进行拟合,以此得到周期性位移。其次,对位移监测数据进行分析,选取周期性位移的影响因子,分别通过GWO-ELM、极限学习机(ELM)和灰狼优化的支持向量机(GWO-SVM)模型对周期性位移进行预测。研究结果表明:GWO-ELM预测模型具有良好的泛化能力,能有效减少人为误差,在预测精度上,明显优于ELM和GWO-SVM模型。基于时间序列与GWO-ELM位移预测模型具有较高的预测精度和泛化能力,是一种有效的滑坡位移预测方法。
Considering the landslide displacement characteristics of the Three Gorges Reservoir Area, a displacement prediction model based on time series and Extreme Learning Machine with Grey Wolves Optimization(GWO-ELM) was proposed to predict the Baishuihe Landslide. Firstly, based on the intrinsic evolution of landslides and external factors, a time series model of landslide prediction was established. The monitoring displacement was decomposed into trend displacement and periodic displacement, and the trend displacement was fitted by a cubic polynomial with a robust weighted least square method to obtain a periodic displacement. Secondly, the periodic displacement was predicted respectively by the GWO-ELM, the separate ELM and the GWO-SVM model through analyzing the influencing factors.The results show that the GWO-ELM prediction model has good generalization ability and it can reduce human error effectively. In terms of the prediction accuracy, GWO-ELM prediction model is apparently more precise than the ELM and GWO-SVM models. Based on the time series and the GWO-ELM model, the proposed model embodies a higher prediction accuracy and has generalization ability, so it is an effective landslide displacement prediction method.
Considering the landslide displacement characteristics of the Three Gorges Reservoir Area, a displacement prediction model based on time series and Extreme Learning Machine with Grey Wolves Optimization(GWO-ELM) was proposed to predict the Baishuihe Landslide. Firstly, based on the intrinsic evolution of landslides and external factors, a time series model of landslide prediction was established. The monitoring displacement was decomposed into trend displacement and periodic displacement, and the trend displacement was fitted by a cubic polynomial with a robust weighted least square method to obtain a periodic displacement. Secondly, the periodic displacement was predicted respectively by the GWO-ELM, the separate ELM and the GWO-SVM model through analyzing the influencing factors.The results show that the GWO-ELM prediction model has good generalization ability and it can reduce human error effectively. In terms of the prediction accuracy, GWO-ELM prediction model is apparently more precise than the ELM and GWO-SVM models. Based on the time series and the GWO-ELM model, the proposed model embodies a higher prediction accuracy and has generalization ability, so it is an effective landslide displacement prediction method.
引文
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[9]HUANG G B,WANG D H,LAN Y.Extreme learning machines:a survey[J].International Journal of Machine Learning&Cybernetics,2011,2(2):107-122.
[10]周超,殷坤龙,黄发明.混沌序列WA-ELM耦合模型在滑坡位移预测中的应用[J].岩土力学,2015,36(9):2674-2680.ZHOU Chao,YIN Kunlong,HUANG Faming.Application of the chaotic sequence WA-ELM coupling model in landslide displacement prediction[J].Rock and Soil Mechanics,2015,36(9):2674-2680.
[11]HUANG G B.An insight into extreme learning machines:random neurons,random features and Kernels[J].Cognitive Computation,2014,6(3):376-390.
[12]MIRJALILI S,MIRJALILI S M,LEWIS A.Grey wolf optimizer[J].Advances in Engineering Software,2014,69(3):46-61.
[13]赵东.基于群智能优化的机器学习方法研究及应用[D].吉林:吉林大学计算机科学与技术学院,2017:1-2.ZHAO Dong.Research and application of machine learning method based on swarm intelligence optimization[D].Jilin:Jilin University.College of Computer Science and Technology,2017:1-2.
[14]彭令,牛瑞卿,吴婷.时间序列分析与支持向量机的滑坡位移预测[J].浙江大学学报(工学版),2013,47(9):1672-1679.PENG Ling,NIU Ruiqing,WU Ting.Time series analysis and support vector machine for landslide displacement prediction[J].Journal of Zhejiang University(Engineering Science),2013,47(9):1672-1679.
[15]张俊,殷坤龙,王佳佳,等.基于时间序列与PSO-SVR耦合模型的白水河滑坡位移预测研究[J].岩石力学与工程学报,2015,34(2):382-391.ZHANG Jun,YIN Kunlong,WANG Jiajia,et al.Displacement prediction of Baishuihe landslide based on time series and PSO-SVR model[J].Chinese Journal of Rock Mechanics and Engineering,2015,34(2):382-391.
[16]WANG Xinying,HAN Min.Online sequential extreme learning machine with kernels for nonstationary time series prediction[J].Neurocomputing,2014,145:90-97.
[17]卢书强,易庆林,易武,等.库水下降作用下滑坡动态变形机理分析-以三峡库区白水河滑坡为例[J].工程地质学报,2014,22(5):869-875.LU Shuqiang,YI Qinglin,YI Wu,et al.Study on dynamic deformation mechanism of landslide in drawdown of reservoir water level-Take Baishuihe landslide in three gorges reservoir area for example[J].Journal of Engineering Geology,2014,22(5):869-875.
[18]桂蕾,殷坤龙.基于多重分形理论的滑坡地表监测位移分析[J].中南大学学报(自然科学版),2014,45(11):3908-3914.GUI Lei,YIN Kunlong.Analysis of landslide surface monitoring displacement based on multi-fractal theory[J].Journal of Central South University(Science and Technology),2014,45(11):3908-3914.
[19]刘磊,殷坤龙,徐勇,等.考虑降雨及库水位变动的区域滑坡灾害稳定性评价研究[J].岩石力学与工程学报,2018,37(2):403-414.LIU Lei,YIN Kunlong,XU Yong,et al.Evaluation of regional landslide stability considering rainfall and variation of water level of reservoir[J].Chinese Journal of Rock Mechanics and Engineering,2018,37(2):403-414.
[20]汪洋,殷坤龙,安关峰.滑坡敏感因子的灰色关联分析[J].岩土力学,2004,25(1):91-93.WANG Yang,YIN Kunlong,AN Guanfeng.Grey correlation analysis of sensitive factors of landslide[J].Rock and Soil Mechanics,2004,25(1):91-93.
[2]许强,汤明高,徐开祥,等.滑坡时空演化规律及预警预报研究[J].岩石力学与工程学报,2008,27(6):1104-1112.XU Qiang,TANG Minggao,XU Kaixiang,et al.Research on space-time evolution laws and early warning-prediction of landslides[J].Chinese Journal of Rock Mechanics and Engineering,2008,27(6):1104-1112.
[3]李骅锦,许强,何雨森,等.“阶跃式”滑坡位移预测及阈值分析的ARMA-(LASSO-ELM)-Copula模型[J].岩石力学与工程学报,2017,36(a2):4075-4084.LI Huajin,XU Qiang,HE Yusen,et al.An ARMA-(LASSO-ELM)-Copula framework for landslide displacement prediction and threshold computing of the displacement of step-like landslides[J].Chinese Journal of Rock Mechanics and Engineering,2017,36(a2):4075-4084.
[4]苗发盛,吴益平,谢媛华,等.基于多算法参数优化与SVR模型的白水河滑坡位移预测[J].工程地质学报,2016,24(6):1136-1144.MIAO Fasheng,WU Yiping,XIE Yuanhua,et al.Dis-placement prediction of Baishuihe landslide based on multi algorithm optimization and SVR model[J].Journal of Engineering Geology,2016,24(6):1136-1144.
[5]吴益平,滕伟福,李亚伟.灰色-神经网络模型在滑坡变形预测中的应用[J].岩石力学与工程学报,2007,26(3):632-636.WU Yiping,TENG Weifu,LI Yawei.Application of grey-neural network model to landslide deformation prediction[J].Chinese Journal of Rock Mechanics and Engineering,2007,26(3):632-636.
[6]肖云,李先福.基于优化的灰色GM模型的滑坡预测[J].武汉工程大学学报,2012,34(1):31-35.XIAO Yun,LI Xianfu.Forecast for landslide based on optimum grey model[J].Wuhan Institute of Technology,2012,34(1):31-35.
[7]殷坤龙.滑坡灾害预测预报[M].武汉:中国地质大学出版社,2004.YIN Kunlong.Landslide hazard prediction and evaluation[M].Wuhan:China University of Geosciences Press,2004.
[8]赵艳南,牛瑞卿,彭令,等.基于粗糙集和粒子群优化支持向量机的滑坡变形预测[J].中南大学学报(自然科学版),2015,46(6):2324-2332.ZHAO Yannan,NIU Ruiqing,PENG Ling,et al.Prediction of landslide deformation based on rough sets and particle swarm optimization-support vector machine[J].Journal of Central South University(Science and Technology),2015,46(6):2324-2332.
[9]HUANG G B,WANG D H,LAN Y.Extreme learning machines:a survey[J].International Journal of Machine Learning&Cybernetics,2011,2(2):107-122.
[10]周超,殷坤龙,黄发明.混沌序列WA-ELM耦合模型在滑坡位移预测中的应用[J].岩土力学,2015,36(9):2674-2680.ZHOU Chao,YIN Kunlong,HUANG Faming.Application of the chaotic sequence WA-ELM coupling model in landslide displacement prediction[J].Rock and Soil Mechanics,2015,36(9):2674-2680.
[11]HUANG G B.An insight into extreme learning machines:random neurons,random features and Kernels[J].Cognitive Computation,2014,6(3):376-390.
[12]MIRJALILI S,MIRJALILI S M,LEWIS A.Grey wolf optimizer[J].Advances in Engineering Software,2014,69(3):46-61.
[13]赵东.基于群智能优化的机器学习方法研究及应用[D].吉林:吉林大学计算机科学与技术学院,2017:1-2.ZHAO Dong.Research and application of machine learning method based on swarm intelligence optimization[D].Jilin:Jilin University.College of Computer Science and Technology,2017:1-2.
[14]彭令,牛瑞卿,吴婷.时间序列分析与支持向量机的滑坡位移预测[J].浙江大学学报(工学版),2013,47(9):1672-1679.PENG Ling,NIU Ruiqing,WU Ting.Time series analysis and support vector machine for landslide displacement prediction[J].Journal of Zhejiang University(Engineering Science),2013,47(9):1672-1679.
[15]张俊,殷坤龙,王佳佳,等.基于时间序列与PSO-SVR耦合模型的白水河滑坡位移预测研究[J].岩石力学与工程学报,2015,34(2):382-391.ZHANG Jun,YIN Kunlong,WANG Jiajia,et al.Displacement prediction of Baishuihe landslide based on time series and PSO-SVR model[J].Chinese Journal of Rock Mechanics and Engineering,2015,34(2):382-391.
[16]WANG Xinying,HAN Min.Online sequential extreme learning machine with kernels for nonstationary time series prediction[J].Neurocomputing,2014,145:90-97.
[17]卢书强,易庆林,易武,等.库水下降作用下滑坡动态变形机理分析-以三峡库区白水河滑坡为例[J].工程地质学报,2014,22(5):869-875.LU Shuqiang,YI Qinglin,YI Wu,et al.Study on dynamic deformation mechanism of landslide in drawdown of reservoir water level-Take Baishuihe landslide in three gorges reservoir area for example[J].Journal of Engineering Geology,2014,22(5):869-875.
[18]桂蕾,殷坤龙.基于多重分形理论的滑坡地表监测位移分析[J].中南大学学报(自然科学版),2014,45(11):3908-3914.GUI Lei,YIN Kunlong.Analysis of landslide surface monitoring displacement based on multi-fractal theory[J].Journal of Central South University(Science and Technology),2014,45(11):3908-3914.
[19]刘磊,殷坤龙,徐勇,等.考虑降雨及库水位变动的区域滑坡灾害稳定性评价研究[J].岩石力学与工程学报,2018,37(2):403-414.LIU Lei,YIN Kunlong,XU Yong,et al.Evaluation of regional landslide stability considering rainfall and variation of water level of reservoir[J].Chinese Journal of Rock Mechanics and Engineering,2018,37(2):403-414.
[20]汪洋,殷坤龙,安关峰.滑坡敏感因子的灰色关联分析[J].岩土力学,2004,25(1):91-93.WANG Yang,YIN Kunlong,AN Guanfeng.Grey correlation analysis of sensitive factors of landslide[J].Rock and Soil Mechanics,2004,25(1):91-93.